Analyse appartements - Shapash

Interpretation des predictions appartements

Project_Information

Author : VotreNom

Description : Rapport Shapash pour appartements

Project_Name : Analyse xgboost_appart


Model analysis

Model used : XGBRegressor

Library : xgboost.sklearn

Library version : 3.0.2

Model parameters :

Parameter key Parameter value
n_estimators 246
objective reg:squarederror
max_depth 10
max_leaves None
max_bin None
grow_policy None
learning_rate 0.037975394322680135
verbosity None
booster None
tree_method None
gamma None
min_child_weight None
max_delta_step None
subsample 0.5396437885733927
sampling_method None
colsample_bytree 0.5870391650248067
colsample_bylevel None
colsample_bynode None
reg_alpha None
reg_lambda None
Parameter key Parameter value
scale_pos_weight None
base_score None
missing nan
num_parallel_tree None
random_state None
n_jobs None
monotone_constraints None
interaction_constraints None
importance_type None
device None
validate_parameters None
enable_categorical False
feature_types None
feature_weights None
max_cat_to_onehot None
max_cat_threshold None
multi_strategy None
eval_metric None
early_stopping_rounds None
callbacks None
_Booster

Dataset analysis

Global analysis

Training dataset Prediction dataset
number of features NaN 56
number of observations NaN 2,759
missing values NaN 0
% missing values NaN 0

Univariate analysis

etage - Numeric

Prediction dataset
count 2,759
mean -0.0135
std 0.976
min -0.514
25% -0.514
50% -0.155
75% 0.205
max 17.5

Target analysis

prix_m2_vente - Numeric

Prediction dataset
count 2,759
mean 2,550
std 1,070
min 216
25% 1,710
50% 2,440
75% 3,300
max 7,440

Multivariate analysis


Model explainability

Note : the explainability graphs were generated using the test set only.

Global feature importance plot

Features contribution plots

etage -


Model performance

Univariate analysis of target variable

prix_m2_vente - Numeric

True values Prediction values
count 2,759 2,759
mean 2,550 2,560
std 1,070 902
min 216 747
25% 1,710 1,840
50% 2,440 2,450
75% 3,300 3,200
max 7,440 6,730

Metrics

MAE : 349

R2 : 0.783

MSE : 250,000

MAPE : 0.172

MdAE : 244

Explained Variance : 0.783